Validating a spatio-temporal model of observed neighborhood physical disorder

Abstract

This study tested spatio-temporal model prediction accuracy and concurrent validity of observed neighborhood physical disorder collected from virtual audits of Google Street View streetscapes. We predicted physical disorder from spatio-temporal regression Kriging models based on measures at three dates per each of 256 streestscapes (n = 768 data points) across an urban area. We assessed model internal validity through cross validation and external validity through Pearson correlations with respondent-reported perceptions of physical disorder from a breast cancer survivor cohort. We compared validity among full models (both large- and small-scale spatio-temporal trends) versus large-scale only. Full models yielded lower prediction error compared to large-scale only models. Physical disorder predictions were lagged at uniform distances and dates away from the respondent-reported perceptions of physical disorder. Correlations between perceived and observed physical disorder predicted from the full model were higher compared to that of the large-scale only model, but only at locations and times closest to the respondent's exact residential address and questionnaire date. A spatio-temporal Kriging model of observed physical disorder is valid.